Docs For The Long Run

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Every Buzzfeed writer out there will tell you the same thing- it is all about the headline when it comes to hooking readers. And by god, I have done it again with this beauty. For you nimrod’s* out there who need explaining to, this title is obviously a play on the best-selling financial book Stocks for the Long Run by economist Jeremy Siegel (UPENN product)…because this blog post includes a discounted cash-flow analysis of doctors’ earnings. Aaaaaaaaaanyway, on to the “fun.”

Besides the fact that I like making excel models (don’t blame me, my mom dropped me on an abacus as a baby), this post was triggered this past weekend when a good friend notified me of his acceptance into a medical fellowship program.  Though I knew he was applying, somehow I was struck by how odd it seemed to have friends still in “training,” waiting for their professional careers to begin. Upon review, I realized that I actually did not know a single fully-practicing doctor in my peer group, despite graduating almost eight years ago. Despite laying the odd-egg, none of us are spring chickens anymore and that seemed like a long time to put off earning that top Doctor Dollar, so I wanted to dust off the ol’ “Alt-O+C+A” (that auto-adjusts column width btw), and see for myself what it meant.

One last caveat – I know analyzing other folks’ earnings is not a good recipe for making/keeping friends so let me throw in the obligatory note that I know there are many very good reasons to become a doctor other than money and I am grateful so many choose the profession. I know my life’s work to date hasn’t exactly earned me a spot with FSM in the afterlife (if you need to ask who FSM is you aren’t going to be there either, so don’t worry).

The Method: (entire model with all assumptions linked here)

I went straight up discounted cash flow (DCF) on this biatch. For those unfamiliar with this approach, the very simple version is that you take money earned in future years and discount it back to what it is worth today. So for example, presuming you can get a 10% return in the stock market, $1.10 one year from now is worth $1 today.

The assumptions and sources for my model were:

  • How much does medical school cost
    • Note I have not included interest on medical school loans since that is just deferring payment, so if the interest rate equals my discount rate it is the same as paying the full amount during medical school
  • How long do doctors train by specialty
  • How much are doctors paid in residency/fellowship, upon first starting to practice, and mid-career by specialty. I ended up cutting the analysis by three groups:
    • Top Tercile (third) Earning Doctors – Assumed 7 years of residency/fellowship. This is mostly surgeons/radiologists and some specialists.
    • Mid Tercile Earning Doctors – Assumed 5 years of residency/fellowship. Mostly non-surgery specialists.
    • Bottom Tercile Earning Doctors- Mostly pediatric, family medicine, and internal medicine.

Lastly, I needed to compare these discounted cash-flows to some alternate career path. Presuming our would-be doctors are smarter than the average bear, I picked the national median average for engineers as a comparable occupation. What the WSJ says that looks like is:

  • $60,000 salary out of college
  • Ramp up to $105,000 salary 20 years into your career

As a side note, I was shocked this average was so low, but that is what living in San Francisco for five years will do to you.
The Madness (The Results):

I charted the results as the post-tax, cumulative earnings, discounted to present day (2015) dollars. Other than medical school, there are no expenses included in this analysis since the point is to see the difference in cumulative post-tax earnings between a doctor and an alternate profession. Since both have to buy food, rent, etc., that is a wash between the two and I can just focus on earnings after I account for medical school. (PLEASE CLICK TO ENLARGE)

3yr 3

5yr 3

7 yr 3

Interpretation/Commentary:

I would love to have folks leave comments on whether the results surprised anyone but these are my key takeaways:

  • It takes a long time to get back to even: Since higher paying specialties require more years of training, I found it interesting to see that they all breakeven (relative to the alternate profession) around the same time, at 18-21 years. That said, ~20 years feels like a long time to me since that means you are hitting 40 before you actually have more money than you would have in an alternate life being a middling engineer likely working a 9-5, 5-day-a-week existence.
  • I want to be a top-tercile earner or the alternate engineering-role: Obviously easier said than done and there are many work-life consideration I have not factored in- but based on the pure monetary options here, existing in the 2nd and 3rd tercile of earners “only” gets you $200k-250k more 2015 dollars across your entire working career. Unfortunately nowadays that only pays for one incremental kid’s private college tuition and/or a nicer house in exchange for decades of study/hard work and professional rigidity. While perhaps still not for me, I can see the lure of almost doubling your cumulative post-tax lifetime earnings and getting $600,000 more 2015 dollars, which puts you in the “Mercedes out front, nice house, kids-go-to private-school” camp .

Is this common knowledge? Do pre-med’s factor this in?

In my estimation: No. Heuristically it rings true that “doctor” is thought of as among the best-paying occupations in the world. I went to a Jewish pre-school, trust me, it starts early. Upon doing a little research, it appears that doctors themselves enter the profession with that same impression. A recent survey by Medscape indicated that almost half of all doctors regret going into medicine, with pay (especially in light of recent cut-backs) being a primary driver of that displeasure.

In addition to many pre-meds perhaps not having the clearest view on the monetary outlook of their careers, my impression has always been that choosing to be a doctor has a huge amount of preselection bias. By that I mean that you already have to be somewhat wary when a profession requires people to essentially opt-in at the age of 18. Additionally, if it is the type of profession which young children are familiar with and can start to identify with at an early age, it only increases the chance that they become fixated on it to the exclusion of other options. By contrast, no small child has ever wanted to be a “alternative payments-platform salesman” or an “educational-tech operations specialist” or a “mid-cap private equity associate.” One piece of evidence for this is the inordinate number of doctors who have at least one doctor parent. A NYT article cited a Mount Sinai physician who found that one third of residents had a doctor parent and estimated the national average to be closer to 20%.

Final Thoughts:

Some readers may be doubting my subjectivity since every over-wrought analysis arrives at a conclusion which seemingly reaffirms the decisions I personally have made in life. I promise there is a very good reason for this. Believe it or not, I actually think through my life in the nerdy, utilitarian, overly-egged-pudding manner which my blog maps out. I am sure my ex-girlfriends can attest to how attractive that is in a man.

Again, no dis-respect to all the doctors out there, between my Indian genes,  idea of “red-blooded American fun,” and riding a motorcycle, I likely will need you guys at some point.

*for you dedicated readers who made it to the bottom of the post, I have a mediocre trivia morsel for you to regurgitate to friends: Nimrod is actually an Old Testament king renowned for his hunting prowess. Hence Bugs Bunny referred to Elmer Fudd as “Nimrod” in cartoons, which has historically been mistaken as an insult, and thus nimrod is often used today to mean “stupid or dense.” Again, very attractive of me.

Thoughtful Sheep

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A radio interview with William Deresiewicz, author of “Excellent Sheep: The Miseducation of the American Elite,” recently caught my ear and got me thinking about the homogeneity in work occupations I see amongst my peer group. It prompted the question- was this due to “risk aversion” or just plain common sense?

The Case for “Risk Averse Sheep”

In a nutshell, Deresiewicz claims in his book that America’s “elite” institutions foster an environment of risk aversion, conformity and the mindless accumulation of awards/grades in order to secure high-paying jobs while discouraging students from exploring careers in the humanities as well as discourse on what a meaningful life actually entails. In support of his arguments he cites, among other pieces of evidence, his own two decades of experience first as a student and then as a faculty member at Columbia and Yale (including working on Yale’s Admissions staff), as well as the notable shift in college majors away from the humanities towards business and technical degrees.  As he notes in a related article The Disadvantages of an Elite Education “The college career office has little to say to students not interested in law, medicine, or business, and elite universities are not going to do anything to discourage the large percentage of their graduates who take their degrees to Wall Street.”

Personally, I must say that much of what Deresiewicz notes about the final, observable outcomes rings true. Every UPENN classmate of mine was intent on achieving good grades. Almost every single one left college into the role of either lawyer, doctor, engineer, marketer, salesman, banker, or consultant. Missing were the actors, musicians, writers, poets, and artists of the world.  If I extend that more broadly to my circle of friends today, I cannot say that much has changed.  Where I differ from Deresiewicz, however, is in the posited motivations for these actions. I think this distribution has everything to do individuals being thoughtful about their life choices and little to do with risk aversion.

The Case for “Thoughtful Sheep”

Rather than make this a qualitative argument, I want to tackle this from a utilitarian angle. The first item then, is to come to a clear understanding of what “risk aversion” means. Risk aversion is simply an individual’s preference for an outcome with a guaranteed payout over an alternative set of outcomes with a higher expected payout but greater uncertainty. To illustrate, if I present two offers:

  1. I give you $95,000 for certain right now, or
  2. The chance to win $200,000 or $0 based on calling a coin flip correctly

Then anyone who would go with option 1) is risk averse. To be clear, human nature, and the entirety of economic theory, is predicated on the idea that almost all humans are risk averse (no matter what you see on Deal or No Deal). The entire insurance industry, in fact, requires that this be true. A risk averse utility curve below illustrates that point:

concave-utility-function

Simply put, the chart above illustrates that given the option of two states (here noted as “Base” and “Base + Bonus”), an individual’s average expected utility will lie somewhere on a line between those two points. To be clear, an individual can only ever be at one of the two points, the line represents their average expected utility . Where on the line that falls depends on the expected likelihood of each state. At every likelihood, however, the expected utility lies below that individual’s utility curve. The downward “bend” of this curve is the decreasing marginal utility we experience with wealth accumulation, which is the very essence of risk aversion.  As a result, this risk-averse individual will get greater utility by purchasing insurance to guarantee a given outcome (i.e., sit at the point on the curve above “x”) rather than just getting their average expected utility (i.e., “x”).

What does all this have to do with pursuing an acting career vs. going into law? Here the “boom or bust” acting career is contrasted against the “insurance” of the middling, and much more certain option of going into law. To be clear the “boom” payout of acting is not necessarily only from money but from the satisfaction of pursuing a dream, so it can be substituted with other careers in the humanities which do not necessarily have the option of million-dollar pay days.

What does this have to do with the perceived risk aversion of graduates of “elite vs. non-elite” institutions? The math follows that the greater the utility of the “certain” outcome for a graduate, the more difficult it will be for him or her to choose to roll the dice and “chase their dreams.” If Student A and Student B both are considering moving to Hollywood to pursue an acting career with estimated outcomes (non-monetary utility included) of:

  1. Being broke: $0 payout, 99% likelihood
  2. Being a successful movie actor:  $8 MM annual payout, 1% chance

The expected payout (utility) here is $80,000 per year for both students. However the critical difference comes from each student’s “insurance” option. Student A has a “certain” job option of reaching the Manager position at a local accounting firm and earning $70,000 a year while Student B comes from an “elite” university and has a “certain” job option of being a mid-level contracts lawyer earning $100,000 a year (net law school expense). Student’s A’s specific level of risk aversion will determine whether running off to Hollywood is a good idea, while Student B doesn’t even have to consider their own degree of risk aversion to know that doing so would lower their average expected utility.

In summary, I think that even if the student populations of “elite institutions” have the same proportion of “dreamers” as the broader population, higher-paying job opportunities ensure that a much smaller proportion of students actually make the decision to chase those dreams. The average starting salary of US graduates was $45,000 in 2013.  For Ivy League graduates, it was $62,000 – 37% higher.

Lastly, an interesting secondary result of this type of decision making is that those individuals who come from elite universities who do choose to chase their “binary” dream job likely only do so because they have a much higher-than-average estimation of their own likelihood of success (e.g., 20% chance of “making it” vs. the normal 1%) which then pushes the math in favor of making the jump. Though totally anecdotal, I believe I have seen this effect first-hand. In my stand-up comedy group from college, an astonishingly high-proportion of those who try to “go professional” succeed, while those who believe they likely do not have what it takes become “professionals” with poorly thought out blogs.